Health and safety risks associated with public injecting among people who inject drugs in <scp>B</scp>angkok, <scp>T</scp>hailand
Bibliographic record
Abstract
INTRODUCTION AND AIMS: The injection of illicit drugs in public spaces is known to pose significant health risks to people who inject drugs (IDU). However, to our knowledge this practice has not been explored in the Asian context. Therefore, we sought to characterise the prevalence of and factors associated with public injecting among a community-recruited sample of IDU in Bangkok, Thailand. DESIGN AND METHODS: Data were derived from the Mitsampan Community Research Project between July and October 2011. Using multivariate logistic regression, this cross-sectional study examined the prevalence and correlates of public injecting within the past six months among 437 IDU participants. RESULTS: In total, 121 (27.7%) participants reported injecting drugs in a public space within the past six months. In multivariate analyses, public drug injection was independently associated with male gender [adjusted odds ratio (AOR) 2.51, 95% confidence interval (CI)) 1.29-5.22], weekly heroin injection (AOR 2.19, 95% CI 1.27-3.77), assisted injection (AOR 1.93, 95% CI 1.06-3.49), rushed injection (AOR 4.36, 95% CI 2.65-7.24), incarceration (AOR 2.27, 95% CI 1.01-5.04) and noticing police presence where drugs are bought or used (AOR 1.83, 95% CI 1.06-3.19). DISCUSSION AND CONCLUSION: A substantial proportion of Thai IDU in our sample reported recent public drug injection. This behaviour was independently associated with a wide range of individual and contextual factors that pose significant health and safety risks to the IDU. These findings highlight the importance of addressing the broader social and physical risk environment surrounding IDU as a means of preventing negative health outcomes among this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".